Publication: Modular Sumcheck Proofs With Applications to Machine Learning and Image Processing
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2023-11-21
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Association for Computing Machinery (ACM)
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Abstract
Cryptographic proof systems provide integrity, fairness, and privacy in applications that outsource data processing tasks. However, general-purpose proof systems do not scale well to large inputs. At the same time, ad-hoc solutions for concrete applications - e.g., machine learning or image processing - are more efficient but lack modularity, hence they are hard to extend or to compose with other tools of a data-processing pipeline. In this paper, we combine the performance of tailored solutions with the versatility of general-purpose proof systems. We do so by introducing a modular framework for verifiable computation of sequential operations. The main tool of our framework is a new information-theoretic primitive called Verifiable Evaluation Scheme on Fingerprinted Data (VE) that captures the properties of diverse sumcheck-based interactive proofs, including the well-established GKR protocol. Thus, we show how to compose VEs for specific functions to obtain verifiability of a data-processing pipeline. We propose a novel VE for convolution operations that can handle multiple input-output channels and batching, and we use it in our framework to build proofs for (convolutional) neural networks and image processing. We realize a prototype implementation of our proof systems, and show that we achieve up to 5x faster proving time and 10x shorter proofs compared to the state-of-the-art, in addition to asymptotic improvements.
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Proof systems, Verifiable computation, Zero-knowledge proofs, Machine learning, Convolutional neural networks, Image processing
Bibliographic citation
Balbás, D., Fiore, D., González Vasco, M. I., Robissout, D., Soriente, C. (26-30 november 2023). Modular Sumcheck Proofs With Applications to Machine Learning and Image Processing [proceedings]. CCS '23: 2023 ACM SIGSAC Conference on Computer and Communications Security, Copenhagen, Denmarck